Deep Learning Techniques for Forest Parameter Retrieval and Accurate Tree Modeling from Remote Sensing Data

Dear Colleagues,

Deep learning and digital twin technologies have the potential to retrieve forest parameters and simulate the forest life cycle, which is beneficial for forest silvicultural management and tree phenotypic trait characterization. While the foundations of these technologies have been laid through proof-of-concept studies, we are now in a position to make transformative advances, especially in the forest studies.

In this issue, we welcome all studies which deploy deep learning technologies and digital twin techniques in forestry applications. We intend to cover some aspects including various remote sensing data analysis, deep learning method development, key issue remedy and forest scenario rendering, along with affording inspiration and heuristic concepts in the multidisciplinary field for promoting the implementation of the technologies in forestry.

Specific topics include, but are not limited to:

• Demonstration of deep learning methodologies for processing forest remote sensing data
• Software approaches to forest visualization and modeling
• Comparison between deep learning methods and other algorithms in forest survey
• Forest scenario reconstruction from LiDAR data or other remote sensing data
• Virtual forest management based on the virtual reality technology
• Computer graphics or machine vision algorithms to enhance the fidelity of the reproduced forest environment
• Prediction of the variations in forest growth properties based on deep learning frameworks from remote sensing data
• Application of multi-remote sensing data in combination with deep learning frameworks for forestry carbon sink measurement
• Processing terminal forest data acquired from various peripherals using deep learning approaches

Prof. Dr. Ting Yun
Dr. Eben Broadbent
Prof. Dr. Huaiqing Zhang
Prof. Dr. Ling Jiang
Guest Editors

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